Analysis date: 2023-10-18

Depends on

CRC_Xenografts_Batch2_DataProcessing Script

load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")

TODO

Setup

Load libraries and functions

Analysis

DEP

Tyrosine

E vs ctrl

data_diff_E_vs_ctrl_pY <- test_diff(pY_se_Set4, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_E_vs_ctrl_pY <- add_rejections_SH(data_diff_E_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pY, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set4_form, dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## Loading required namespace: reactome.db
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## [1] "Signal Transduction"                                
## [2] "Insulin receptor signalling cascade"                
## [3] "GPCR downstream signalling"                         
## [4] "Golgi Cisternae Pericentriolar Stack Reorganization"
## [5] "Tight junction interactions"
PTM-SEA
GSEA_E_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ERLOTINIB"               "PATH-NP_EGFR1_PATHWAY"           
## [3] "PERT-PSP_FGF1"                    "PERT-PSP_IMATINIB"               
## [5] "PERT-PSP_PAR1_ACTIVATING_PEPTIDE" "PERT-P100-DIA2_VEMURAFENIB"      
## [7] "KINASE-PSP_EphA2/EPHA2"
GSEA_E_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 67 × 8
##    pathway                    pval    padj log2err    ES   NES  size leadingEdge
##    <chr>                     <dbl>   <dbl>   <dbl> <dbl> <dbl> <int> <list>     
##  1 PERT-PSP_ERLOTINIB      1.25e-5 0.00222   0.593 0.846  2.12     8 <chr [7]>  
##  2 PATH-NP_EGFR1_PATHWAY   7.06e-6 0.00222   0.611 0.432  1.97   110 <chr [48]> 
##  3 PERT-PSP_IL_2           1.36e-3 0.0101    0.455 0.794  1.82     6 <chr [4]>  
##  4 PERT-PSP_FGF1           8.49e-5 0.00436   0.538 0.976  1.77     3 <chr [3]>  
##  5 PERT-PSP_IMATINIB       8.49e-5 0.00436   0.538 0.976  1.77     3 <chr [3]>  
##  6 KINASE-iKiP_EGFR        1.58e-3 0.0115    0.455 0.734  1.75     7 <chr [4]>  
##  7 PERT-PSP_PAR1_ACTIVATI… 3.43e-4 0.00436   0.498 0.959  1.74     3 <chr [3]>  
##  8 PATH-NP_TSLP_PATHWAY    5.94e-3 0.0400    0.407 0.603  1.72    12 <chr [7]>  
##  9 PERT-PSP_AG1478         1.60e-3 0.0115    0.455 0.927  1.68     3 <chr [2]>  
## 10 KINASE-iKiP_PDGFRA      8.73e-3 0.0465    0.381 0.745  1.61     5 <chr [3]>  
## # ℹ 57 more rows
Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ERLOTINIB"         "PATH-NP_EGFR1_PATHWAY"     
## [3] "PERT-PSP_FGF1"              "PERT-PSP_IMATINIB"         
## [5] "PERT-P100-DIA2_VEMURAFENIB" "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 250 × 4
##    HGNC_Symbol Annotated_Sequence  MOD_RSD    FC
##    <chr>       <chr>               <chr>   <dbl>
##  1 CDK1        IGEGTYGVVyKGR       Y19-p    1.78
##  2 MAPK3       IADPEHDHTGFLTEyVATR Y204-p   1.36
##  3 MAPK3       IADPEHDHTGFLtEyVATR Y204-p   1.36
##  4 MAPK3       iADPEHDHTGFLTEyVATR Y204-p   1.36
##  5 PTK6        ERLSSFTSyENPT       Y447-p   1.31
##  6 PTK6        LSSFTSyENPT         Y447-p   1.31
##  7 PTK6        eRLSSFTSyENPT       Y447-p   1.31
##  8 PTK6        lSSFTSyENPT         Y447-p   1.31
##  9 PTPRA       VVQEYIDAFSDyANFK    Y798-p   1.23
## 10 PTPRA       vVQEYIDAFSDyANFk    Y798-p   1.23
## # ℹ 240 more rows
Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ERLOTINIB"               "PATH-NP_EGFR1_PATHWAY"           
## [3] "PERT-PSP_FGF1"                    "PERT-PSP_IMATINIB"               
## [5] "PERT-PSP_PAR1_ACTIVATING_PEPTIDE" "PERT-P100-DIA2_VEMURAFENIB"      
## [7] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 81 × 4
##    HGNC_Symbol Annotated_Sequence            MOD_RSD    FC
##    <chr>       <chr>                         <chr>   <dbl>
##  1 CDH1        yLPRPANPDEIGNFIDENLK          Y797-p   1.25
##  2 CDH1        yLPRPANPDEIGNFIDENLk          Y797-p   1.25
##  3 PTPRA       VVQEYIDAFSDyANFK              Y798-p   1.23
##  4 PTPRA       vVQEYIDAFSDyANFk              Y798-p   1.23
##  5 PAG1        SREEDPTLTEEEISAMySSVNKPGQLVNK Y317-p   1.20
##  6 PAG1        sREEDPTLTEEEISAMySSVNkPGQLVNk Y317-p   1.20
##  7 CTTN        NASTFEDVTQVSSAyQK             Y334-p   1.15
##  8 CTTN        MDKNASTFEDVTQVSSAyQK          Y334-p   1.15
##  9 CTTN        nASTFEDVTQVSSAyQk             Y334-p   1.15
## 10 SHC1        ELFDDPSyVNVQNLDK              Y427-p   1.11
## # ℹ 71 more rows
Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ERLOTINIB"               "PATH-NP_EGFR1_PATHWAY"           
## [3] "PERT-PSP_FGF1"                    "PERT-PSP_IMATINIB"               
## [5] "KINASE-iKiP_EGFR"                 "PERT-PSP_PAR1_ACTIVATING_PEPTIDE"
## [7] "PERT-P100-DIA2_VEMURAFENIB"       "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 14 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD       FC
##    <chr>       <chr>                <chr>      <dbl>
##  1 EPHA2       VLEDDPEATyTTSGGK     Y772-p   0.316  
##  2 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p   0.316  
##  3 EPHA2       vLEDDPEATyTTSGGk     Y772-p   0.316  
##  4 EPHA2       vLEDDPEATyTTSGGkIPIR Y772-p   0.316  
##  5 EPHA2       TyVDPHTYEDPNQAVLK    Y588-p   0.258  
##  6 EPHA2       VIGAGEFGEVyKGMLK     Y628-p   0.199  
##  7 EPHA2       QSPEDVyFSK           Y575-p   0.0356 
##  8 EPHA2       qSPEDVyFSk           Y575-p   0.0356 
##  9 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -0.00166
## 10 EPHA2       tYVDPHTyEDPNQAVLk    Y594-p  -0.00166
## 11 EPHA2       YLANMNyVHR           Y735-p  -0.205  
## 12 EPHA2       IAySLLGLK            Y960-p  -0.576  
## 13 CLDN4       SAAASNyV             Y208-p  -1.21   
## 14 CLDN4       sAAASNyV             Y208-p  -1.21

EC vs ctrl

data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set4, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## character(0)
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
PTM-SEA
GSEA_EC_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
GSEA_EC_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 1 × 8
##   pathway                     pval   padj log2err     ES   NES  size leadingEdge
##   <chr>                      <dbl>  <dbl>   <dbl>  <dbl> <dbl> <int> <list>     
## 1 KINASE-PSP_EphA2/EPHA2 0.0000345 0.0123   0.557 -0.887 -2.16     8 <chr [7]>
Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 250 × 4
##    HGNC_Symbol Annotated_Sequence       MOD_RSD    FC
##    <chr>       <chr>                    <chr>   <dbl>
##  1 ENO1        AAVPSGASTGIyEALELRDNDK   Y44-p    1.27
##  2 ENO1        AAVPSGASTGIyEALELRDNDKTR Y44-p    1.27
##  3 ENO1        aAVPSGASTGIyEALELRDNDk   Y44-p    1.27
##  4 PTPRA       VVQEYIDAFSDyANFK         Y798-p   1.08
##  5 PTPRA       vVQEYIDAFSDyANFk         Y798-p   1.08
##  6 DLG3        DNEVDGQDyHFVVSR          Y673-p   1.04
##  7 DLG3        RDNEVDGQDyHFVVSR         Y673-p   1.04
##  8 DLG3        dNEVDGQDyHFVVSR          Y673-p   1.04
##  9 DLG3        rDNEVDGQDyHFVVSR         Y673-p   1.04
## 10 CTTN        LPSSPVyEDAASFK           Y421-p   1.03
## # ℹ 240 more rows
Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 81 × 4
##    HGNC_Symbol Annotated_Sequence               MOD_RSD    FC
##    <chr>       <chr>                            <chr>   <dbl>
##  1 PTPRA       VVQEYIDAFSDyANFK                 Y798-p  1.08 
##  2 PTPRA       vVQEYIDAFSDyANFk                 Y798-p  1.08 
##  3 ARHGAP35    SVSSSPWLPQDGFDPSDyAEPMDAVVKPR    Y1087-p 1.06 
##  4 ARHGAP35    sVSSSPWLPQDGFDPSDyAEPMDAVVkPR    Y1087-p 1.06 
##  5 CTTN        NASTFEDVTQVSSAyQK                Y334-p  1.04 
##  6 CTTN        MDKNASTFEDVTQVSSAyQK             Y334-p  1.04 
##  7 CTTN        nASTFEDVTQVSSAyQk                Y334-p  1.04 
##  8 ARHGAP35    NEEENIySVPHDSTQGK                Y1105-p 0.966
##  9 ARHGAP35    nEEENIySVPHDSTQGk                Y1105-p 0.966
## 10 SRC         EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p  0.880
## # ℹ 71 more rows
Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 14 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD     FC
##    <chr>       <chr>                <chr>    <dbl>
##  1 EPHA2       VIGAGEFGEVyKGMLK     Y628-p  -0.155
##  2 EPHA2       YLANMNyVHR           Y735-p  -0.486
##  3 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -0.526
##  4 EPHA2       tYVDPHTyEDPNQAVLk    Y594-p  -0.526
##  5 EPHA2       VLEDDPEATyTTSGGK     Y772-p  -0.576
##  6 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  -0.576
##  7 EPHA2       vLEDDPEATyTTSGGk     Y772-p  -0.576
##  8 EPHA2       vLEDDPEATyTTSGGkIPIR Y772-p  -0.576
##  9 EPHA2       TyVDPHTYEDPNQAVLK    Y588-p  -0.712
## 10 EPHA2       QSPEDVyFSK           Y575-p  -0.786
## 11 EPHA2       qSPEDVyFSk           Y575-p  -0.786
## 12 EPHA2       IAySLLGLK            Y960-p  -1.57 
## 13 CLDN4       SAAASNyV             Y208-p  -1.79 
## 14 CLDN4       sAAASNyV             Y208-p  -1.79

EBC vs ctrl

data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set4, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## character(0)
PTM-SEA
GSEA_EBC_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
GSEA_EBC_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 1 × 8
##   pathway                    pval    padj log2err     ES   NES  size leadingEdge
##   <chr>                     <dbl>   <dbl>   <dbl>  <dbl> <dbl> <int> <list>     
## 1 KINASE-PSP_EphA2/EPHA2  1.28e-5 0.00457   0.593 -0.858 -2.67     8 <chr [6]>
Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 250 × 4
##    HGNC_Symbol Annotated_Sequence             MOD_RSD    FC
##    <chr>       <chr>                          <chr>   <dbl>
##  1 CTTN        LPSSPVyEDAASFK                 Y421-p   1.52
##  2 CTTN        lPSSPVyEDAASFk                 Y421-p   1.52
##  3 CTTN        TQTPPVSPAPQPTEERLPSSPVyEDAASFK Y421-p   1.52
##  4 NCK1        LyDLNMPAYVK                    Y105-p   1.50
##  5 NCK1        lyDLNMPAYVk                    Y105-p   1.50
##  6 PTK6        ERLSSFTSyENPT                  Y447-p   1.35
##  7 PTK6        LSSFTSyENPT                    Y447-p   1.35
##  8 PTK6        eRLSSFTSyENPT                  Y447-p   1.35
##  9 PTK6        lSSFTSyENPT                    Y447-p   1.35
## 10 ENO1        AAVPSGASTGIyEALELRDNDK         Y44-p    1.33
## # ℹ 240 more rows
Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 81 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD    FC
##    <chr>       <chr>                <chr>   <dbl>
##  1 CTTN        NASTFEDVTQVSSAyQK    Y334-p  1.50 
##  2 CTTN        MDKNASTFEDVTQVSSAyQK Y334-p  1.50 
##  3 CTTN        nASTFEDVTQVSSAyQk    Y334-p  1.50 
##  4 CDH1        yLPRPANPDEIGNFIDENLK Y797-p  1.41 
##  5 CDH1        yLPRPANPDEIGNFIDENLk Y797-p  1.41 
##  6 PTPRA       VVQEYIDAFSDyANFK     Y798-p  1.17 
##  7 PTPRA       vVQEYIDAFSDyANFk     Y798-p  1.17 
##  8 DAPP1       KVEEPSIyESVR         Y139-p  1.01 
##  9 CLTC        SVNESLNNLFITEEDyQALR Y1477-p 0.986
## 10 CLTC        sVNESLNNLFITEEDyQALR Y1477-p 0.986
## # ℹ 71 more rows
Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "KINASE-PSP_EphA2/EPHA2"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 14 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD      FC
##    <chr>       <chr>                <chr>     <dbl>
##  1 EPHA2       VIGAGEFGEVyKGMLK     Y628-p   0.191 
##  2 EPHA2       TyVDPHTYEDPNQAVLK    Y588-p  -0.0782
##  3 EPHA2       VLEDDPEATyTTSGGK     Y772-p  -0.296 
##  4 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  -0.296 
##  5 EPHA2       vLEDDPEATyTTSGGk     Y772-p  -0.296 
##  6 EPHA2       vLEDDPEATyTTSGGkIPIR Y772-p  -0.296 
##  7 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -0.310 
##  8 EPHA2       tYVDPHTyEDPNQAVLk    Y594-p  -0.310 
##  9 EPHA2       YLANMNyVHR           Y735-p  -0.375 
## 10 EPHA2       QSPEDVyFSK           Y575-p  -0.382 
## 11 EPHA2       qSPEDVyFSk           Y575-p  -0.382 
## 12 EPHA2       IAySLLGLK            Y960-p  -0.969 
## 13 CLDN4       SAAASNyV             Y208-p  -1.25  
## 14 CLDN4       sAAASNyV             Y208-p  -1.25

EC vs E

data_diff_EC_vs_E_pY <- test_diff(pY_se_Set4, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## [1] "SHC1 events in EGFR signaling"                      
## [2] "Golgi Cisternae Pericentriolar Stack Reorganization"
## [3] "G alpha (q) signalling events"                      
## [4] "Insulin receptor signalling cascade"
#data_results <- get_df_long(dep)
PTM-SEA
GSEA_EC_vs_E_PTM <- Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY" "KINASE-iKiP_EGFR"      "PERT-PSP_ERLOTINIB"
GSEA_EC_vs_E_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 4 × 8
##   pathway                    pval    padj log2err     ES   NES  size leadingEdge
##   <chr>                     <dbl>   <dbl>   <dbl>  <dbl> <dbl> <int> <list>     
## 1 PATH-NP_EGFR1_PATHWAY 0.000128  0.0229    0.519 -0.472 -1.60   110 <chr [55]> 
## 2 KINASE-iKiP_EGFR      0.000295  0.0264    0.498 -0.861 -1.82     7 <chr [5]>  
## 3 KINASE-PSP_EGFR       0.000294  0.0264    0.498 -0.840 -1.85     8 <chr [5]>  
## 4 PERT-PSP_ERLOTINIB    0.0000157 0.00562   0.576 -0.892 -1.97     8 <chr [7]>
Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-P100-DIA2_VEMURAFENIB" "PATH-NP_EGFR1_PATHWAY"     
## [3] "PERT-PSP_ANTI_CD3"          "KINASE-iKiP_EGFR"          
## [5] "PERT-PSP_ERLOTINIB"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 250 × 4
##    HGNC_Symbol Annotated_Sequence                MOD_RSD    FC
##    <chr>       <chr>                             <chr>   <dbl>
##  1 NCK1        LyDLNMPAYVK                       Y105-p  0.260
##  2 NCK1        lyDLNMPAYVk                       Y105-p  0.260
##  3 PIK3R1      DQyLMWLTQK                        Y580-p  0.238
##  4 PIK3R1      TRDQyLMWLTQK                      Y580-p  0.238
##  5 PIK3R1      dQyLMWLTQk                        Y580-p  0.238
##  6 PIK3R1      dQyLmWLTQk                        Y580-p  0.238
##  7 SRC         EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL  Y530-p  0.223
##  8 SRC         kEPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p  0.223
##  9 STAT3       YCRPESQEHPEADPGSAAPyLK            Y705-p  0.217
## 10 ENO1        AAVPSGASTGIyEALELRDNDK            Y44-p   0.185
## # ℹ 240 more rows
Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY" "PERT-PSP_ANTI_CD3"     "KINASE-iKiP_EGFR"     
## [4] "PERT-PSP_ERLOTINIB"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 81 × 4
##    HGNC_Symbol Annotated_Sequence                    MOD_RSD    FC
##    <chr>       <chr>                                 <chr>   <dbl>
##  1 ARHGAP35    SVSSSPWLPQDGFDPSDyAEPMDAVVKPR         Y1087-p 0.918
##  2 ARHGAP35    sVSSSPWLPQDGFDPSDyAEPMDAVVkPR         Y1087-p 0.918
##  3 ARHGAP35    NEEENIySVPHDSTQGK                     Y1105-p 0.444
##  4 ARHGAP35    nEEENIySVPHDSTQGk                     Y1105-p 0.444
##  5 SDCBP       VDKVIQAQTAFSANPANPAILSEASAPIPHDGNLyPR Y46-p   0.231
##  6 SDCBP       VIQAQTAFSANPANPAILSEASAPIPHDGNLyPR    Y46-p   0.231
##  7 SDCBP       vDkVIQAQTAFSANPANPAILSEASAPIPHDGNLyPR Y46-p   0.231
##  8 SDCBP       vIQAQTAFSANPANPAILSEASAPIPHDGNLyPR    Y46-p   0.231
##  9 SRC         EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL      Y530-p  0.223
## 10 SRC         kEPEERPTFEYLQAFLEDYFTSTEPQyQPGENL     Y530-p  0.223
## # ℹ 71 more rows
Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PATH-NP_EGFR1_PATHWAY" "KINASE-iKiP_EGFR"      "PERT-PSP_ERLOTINIB"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 14 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD     FC
##    <chr>       <chr>                <chr>    <dbl>
##  1 EPHA2       YLANMNyVHR           Y735-p  -0.281
##  2 EPHA2       VIGAGEFGEVyKGMLK     Y628-p  -0.354
##  3 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -0.525
##  4 EPHA2       tYVDPHTyEDPNQAVLk    Y594-p  -0.525
##  5 CLDN4       SAAASNyV             Y208-p  -0.580
##  6 CLDN4       sAAASNyV             Y208-p  -0.580
##  7 EPHA2       QSPEDVyFSK           Y575-p  -0.822
##  8 EPHA2       qSPEDVyFSk           Y575-p  -0.822
##  9 EPHA2       VLEDDPEATyTTSGGK     Y772-p  -0.892
## 10 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  -0.892
## 11 EPHA2       vLEDDPEATyTTSGGk     Y772-p  -0.892
## 12 EPHA2       vLEDDPEATyTTSGGkIPIR Y772-p  -0.892
## 13 EPHA2       TyVDPHTYEDPNQAVLK    Y588-p  -0.970
## 14 EPHA2       IAySLLGLK            Y960-p  -0.996

EBC vs EC

data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set4, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 1 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some of the pathways the P-values were likely overestimated. For
## such pathways log2err is set to NA.
## [1] "Cell-Cell communication"

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)
PTM-SEA
GSEA_EBC_vs_EC_PTM <- Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
GSEA_EBC_vs_EC_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 0 × 8
## # ℹ 8 variables: pathway <chr>, pval <dbl>, padj <dbl>, log2err <dbl>,
## #   ES <dbl>, NES <dbl>, size <int>, leadingEdge <list>
Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 250 × 4
##    HGNC_Symbol Annotated_Sequence MOD_RSD    FC
##    <chr>       <chr>              <chr>   <dbl>
##  1 LCK         SVLEDFFTATEGQyQPQP Y505-p  1.51 
##  2 LCK         sVLEDFFTATEGQyQPQP Y505-p  1.51 
##  3 WASL        VIyDFIEK           Y256-p  0.892
##  4 WASL        ETSKVIyDFIEK       Y256-p  0.892
##  5 WASL        eTSkVIyDFIEk       Y256-p  0.892
##  6 WASL        vIyDFIEk           Y256-p  0.892
##  7 CTNND1      APSRQDVyGPQPQVR    Y257-p  0.859
##  8 CTNND1      APsRQDVyGPQPQVR    Y257-p  0.859
##  9 CTNND1      QDVyGPQPQVR        Y257-p  0.859
## 10 PEAK1       VPIVINPNAyDNLAIYK  Y635-p  0.762
## # ℹ 240 more rows
Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 81 × 4
##    HGNC_Symbol Annotated_Sequence              MOD_RSD    FC
##    <chr>       <chr>                           <chr>   <dbl>
##  1 CFL1        NIILEEGKEILVGDVGQTVDDPyATFVK    Y68-p   0.884
##  2 CFL1        nIILEEGkEILVGDVGQTVDDPyATFVk    Y68-p   0.884
##  3 SHC1        ELFDDPSyVNVQNLDK                Y427-p  0.722
##  4 SHC1        eLFDDPSyVNVQNLDk                Y427-p  0.722
##  5 DAPP1       KVEEPSIyESVR                    Y139-p  0.675
##  6 EGFR        YSSDPTGALTEDSIDDTFLPVPEyINQSVPK Y1092-p 0.616
##  7 PRKCD       KTGVAGEDMQDNSGTyGK              Y334-p  0.603
##  8 PRKCD       TGVAGEDMQDNSGTyGK               Y334-p  0.603
##  9 PRKCD       tGVAGEDMQDNSGTyGk               Y334-p  0.603
## 10 CDH1        yLPRPANPDEIGNFIDENLK            Y797-p  0.583
## # ℹ 71 more rows
Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 14 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD    FC
##    <chr>       <chr>                <chr>   <dbl>
##  1 EPHA2       TyVDPHTYEDPNQAVLK    Y588-p  0.634
##  2 EPHA2       IAySLLGLK            Y960-p  0.602
##  3 CLDN4       SAAASNyV             Y208-p  0.540
##  4 CLDN4       sAAASNyV             Y208-p  0.540
##  5 EPHA2       QSPEDVyFSK           Y575-p  0.404
##  6 EPHA2       qSPEDVyFSk           Y575-p  0.404
##  7 EPHA2       VIGAGEFGEVyKGMLK     Y628-p  0.346
##  8 EPHA2       VLEDDPEATyTTSGGK     Y772-p  0.280
##  9 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  0.280
## 10 EPHA2       vLEDDPEATyTTSGGk     Y772-p  0.280
## 11 EPHA2       vLEDDPEATyTTSGGkIPIR Y772-p  0.280
## 12 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  0.216
## 13 EPHA2       tYVDPHTyEDPNQAVLk    Y594-p  0.216
## 14 EPHA2       YLANMNyVHR           Y735-p  0.111

Serine/Threonine all

Each condition vs ctrl

data_diff_E_vs_ctrl_pST <- test_diff(pST_se_Set4, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_E_vs_ctrl_pST <- add_rejections_SH(data_diff_E_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pST, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set4_form, dep_E_vs_ctrl_pST, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## character(0)
data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set4, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## character(0)
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set4, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## character(0)

EC vs E

data_diff_EC_vs_E_pST <- test_diff(pST_se_Set4, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
## Warning in pvt.fit.nullmodel(x, x0, statistic = statistic): Variance of scale
## parameter set to zero due to numerical problems
dep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## character(0)
#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set4, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## character(0)

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)

Save

rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>% select(HGNC_Symbol, E_vs_ctrl_diff) %>% write.table("../Data/Kinase_enrichment/Batch2_Set4_E_vs_ctrl_pY_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>% select(HGNC_Symbol, EC_vs_ctrl_diff) %>% write.table("../Data/Kinase_enrichment/Batch2_Set4_EC_vs_ctrl_pY_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>% 
  select(HGNC_Symbol, ends_with("_diff")) %>%
  group_by(HGNC_Symbol) %>%
  mutate(abs_FC = abs(E_vs_ctrl_diff) ) %>%
  arrange(desc( abs_FC) ) %>%
  slice(1) %>%
  ungroup() %>%
  select(HGNC_Symbol, ends_with("_diff") ) %>%
  write.table("../Data/Kinase_enrichment/Batch2_Set4_E_vs_ctrl_pY_mostextremeFCperprotein_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>% 
  select(HGNC_Symbol, ends_with("_diff")) %>%
  group_by(HGNC_Symbol) %>%
  mutate(abs_FC = abs(EC_vs_ctrl_diff) ) %>%
  arrange(desc( abs_FC) ) %>%
  slice(1) %>%
  ungroup() %>%
  select(HGNC_Symbol, ends_with("_diff") ) %>%
  write.table("../Data/Kinase_enrichment/Batch2_Set4_EC_vs_ctrl_pY_mostextremeFCperprotein_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>% 
  filter(E_vs_ctrl_diff>1) %>%
  select(HGNC_Symbol ) %>% unique() %>%
  write.table("../Data/Kinase_enrichment/Batch2_Set4_E_vs_ctrl_pY_FCmorethan1_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>% 
  filter(EC_vs_ctrl_diff>1) %>%
  select(HGNC_Symbol ) %>% unique() %>%
  write.table("../Data/Kinase_enrichment/Batch2_Set4_EC_vs_ctrl_pY_FCmorethan1_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

Session Info

sessionInfo()
## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] lubridate_1.9.2             forcats_1.0.0              
##  [3] stringr_1.5.0               dplyr_1.1.2                
##  [5] purrr_1.0.2                 readr_2.1.4                
##  [7] tidyr_1.3.0                 tibble_3.2.1               
##  [9] ggplot2_3.4.2               tidyverse_2.0.0            
## [11] mdatools_0.14.0             SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
## [15] MatrixGenerics_1.10.0       matrixStats_1.0.0          
## [17] DEP_1.20.0                  org.Hs.eg.db_3.16.0        
## [19] AnnotationDbi_1.60.2        IRanges_2.32.0             
## [21] S4Vectors_0.36.2            Biobase_2.58.0             
## [23] BiocGenerics_0.44.0         fgsea_1.24.0               
## 
## loaded via a namespace (and not attached):
##   [1] circlize_0.4.15        fastmatch_1.1-4        plyr_1.8.8            
##   [4] igraph_1.5.1           gmm_1.8                lazyeval_0.2.2        
##   [7] shinydashboard_0.7.2   crosstalk_1.2.0        BiocParallel_1.32.6   
##  [10] digest_0.6.33          foreach_1.5.2          htmltools_0.5.6       
##  [13] fansi_1.0.4            magrittr_2.0.3         memoise_2.0.1         
##  [16] cluster_2.1.4          doParallel_1.0.17      tzdb_0.4.0            
##  [19] limma_3.54.2           ComplexHeatmap_2.14.0  Biostrings_2.66.0     
##  [22] imputeLCMD_2.1         sandwich_3.0-2         timechange_0.2.0      
##  [25] colorspace_2.1-0       blob_1.2.4             xfun_0.40             
##  [28] crayon_1.5.2           RCurl_1.98-1.12        jsonlite_1.8.7        
##  [31] impute_1.72.3          zoo_1.8-12             iterators_1.0.14      
##  [34] glue_1.6.2             hash_2.2.6.2           gtable_0.3.3          
##  [37] zlibbioc_1.44.0        XVector_0.38.0         GetoptLong_1.0.5      
##  [40] DelayedArray_0.24.0    shape_1.4.6            scales_1.2.1          
##  [43] pheatmap_1.0.12        vsn_3.66.0             mvtnorm_1.2-2         
##  [46] DBI_1.1.3              Rcpp_1.0.11            plotrix_3.8-2         
##  [49] mzR_2.32.0             viridisLite_0.4.2      xtable_1.8-4          
##  [52] clue_0.3-64            reactome.db_1.82.0     bit_4.0.5             
##  [55] preprocessCore_1.60.2  sqldf_0.4-11           MsCoreUtils_1.10.0    
##  [58] DT_0.28                htmlwidgets_1.6.2      httr_1.4.6            
##  [61] gplots_3.1.3           RColorBrewer_1.1-3     ellipsis_0.3.2        
##  [64] farver_2.1.1           pkgconfig_2.0.3        XML_3.99-0.14         
##  [67] sass_0.4.7             utf8_1.2.3             STRINGdb_2.10.1       
##  [70] labeling_0.4.2         tidyselect_1.2.0       rlang_1.1.1           
##  [73] later_1.3.1            munsell_0.5.0          tools_4.2.3           
##  [76] cachem_1.0.8           cli_3.6.1              gsubfn_0.7            
##  [79] generics_0.1.3         RSQLite_2.3.1          fdrtool_1.2.17        
##  [82] evaluate_0.21          fastmap_1.1.1          mzID_1.36.0           
##  [85] yaml_2.3.7             knitr_1.43             bit64_4.0.5           
##  [88] caTools_1.18.2         KEGGREST_1.38.0        ncdf4_1.21            
##  [91] mime_0.12              compiler_4.2.3         rstudioapi_0.15.0     
##  [94] plotly_4.10.2          png_0.1-8              affyio_1.68.0         
##  [97] stringi_1.7.12         bslib_0.5.0            highr_0.10            
## [100] MSnbase_2.24.2         lattice_0.21-8         ProtGenerics_1.30.0   
## [103] Matrix_1.6-0           tmvtnorm_1.5           vctrs_0.6.3           
## [106] pillar_1.9.0           norm_1.0-11.1          lifecycle_1.0.3       
## [109] BiocManager_1.30.22    jquerylib_0.1.4        MALDIquant_1.22.1     
## [112] GlobalOptions_0.1.2    data.table_1.14.8      cowplot_1.1.1         
## [115] bitops_1.0-7           httpuv_1.6.11          R6_2.5.1              
## [118] pcaMethods_1.90.0      affy_1.76.0            promises_1.2.1        
## [121] KernSmooth_2.23-22     codetools_0.2-19       MASS_7.3-60           
## [124] gtools_3.9.4           assertthat_0.2.1       chron_2.3-61          
## [127] proto_1.0.0            rjson_0.2.21           withr_2.5.0           
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3         hms_1.1.3             
## [133] grid_4.2.3             rmarkdown_2.23         shiny_1.7.4.1
knitr::knit_exit()